DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
Digital subtraction angiography (DSA) is universally acknowledged as the gold standard for examining lesion angioarchitecture, elucidating arterial blood supply dynamics, and guiding endovascular interventions. The automatic segmentation of intracranial arteries (IA) in DSA, which is pivotal for quantifying vascular morphology, plays an essential role in computer-assisted stroke research and clinical practices. Nevertheless, research in this specific domain remains constrained, primarily owing to the unavailability of publicly datasets for IA segmentation within the research community. Currently, the predominant focus of methodologies lies in the segmentation of single-frame DSA using in-house datasets. These methods, limited by the partial inclusion of contrast in single-frame DSA, encounters challenges in rendering a precise representation of vascular structures. In this paper, we introduces DIAS, a dataset specifically developed for IA segmentation in DSA sequences. A comprehensive benchmark has been established for evaluating DIAS, covering fully, weakly, and semi-supervised segmentation methods. Specifically, we propose a vessel sequence segmentation network that captures the spatiotemporal representation of intravascular contrast for segmenting vessels in DSA sequences. For weakly-supervised learning, we propose a novel scribble learning-based image segmentation framework, incorporating both scribble supervision and consistency regularization. Furthermore, we introduce a random patch-based self-training framework that harnesses unlabeled DSA sequences to improve segmentation performance. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications.
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